Supervisory Control and Data Acquisition (SCADA)
Assembly Line
Industrial Software Helps Pipeline Operators Transition to More Sustainable Operation
For many midstream companies, the key to meeting that challenge lies in increasing flexibility. Some operators are looking at ways to convert their pipelines to transport hydrogen and carbon dioxide (CO2). But transporting these materials that are relatively new to the industry requires new methods of operation and monitoring, which in turn requires increased digitalization. To be successful, teams need a perfectly clear vision of what is going on—all the time.
The key is to implement modern supervisory control and data acquisition (SCADA) solutions to help keep a finger on the pulse of operations. Today’s modern SCADA solutions bring real-time operational data from the field to operators—in the control room, in the field, or wherever else they may be. But to get the most out of those solutions, teams also need powerful industrial software to give them insight into how well their operations perform. One of the most critical tools midstream operations teams need is pipeline management software, for analytics and real-time operational intelligence across their network.
How to calculate digital transformation ROI
To simplify and prioritize the digital vision, first consider how digital transformation for manufacturing integrates three key business components:
- Supervisory control and data acquisition (SCADA), programmable logic controller (PLC) and control (machine automation)
- Manufacturing execution system (MES), which includes: (part traceability, machine monitoring and machine management, i.e., recipes and so on)
- Enterprise resource planning (ERP), which includes: (AP/AR, raw materials, purchase orders, inventory, scheduling and tracking).
Achieving large profitability and competitive gains requires seamless integration of three business components. However, it is important to begin at the machine automation level, then incorporate the MES and finally the ERP. The reason for following this path is based on data requirements but also because it is the easiest path for development.
MES & Machine Learning
As the manufacturing sector continues to embrace digitalization, fully integrated manufacturing execution systems will become more and more useful for managing facilities. However, it is expensive for a plant to fully revamp their IT infrastructure. Manufacturers with partially integrated or non-existent MES won’t upgrade unless there are benefits that outweigh the costs, and returns that can be realized.
Incorporating a MES and subsequent machine learning platform into a facility’s or organization’s infrastructure reduces the cost of manual data processing. Tasks that have traditionally taken hours of manual labor, such as aggregating line data to identify trends, can be automated and completed in minutes or less. In this case, machine learning isn’t competing with statistical process control (SPC) or other traditional quality methods; it’s augmenting them so that engineers spend less time to get better insights into their operations.